FedCrossID: Recovering Fragmented Cross-Camera Supervision in Federated Person Re-Identification

Person re-identification (ReID) relies on recognizing the same person across cameras. In federated-by-camera training, however, each camera uses an independent identity namespace, leaving cross-camera correspondences unobserved. Existing federated ReID methods improve aggregation, domain generalization, or local feature diversity, but generally leave unknown correspondences between partially overlapping client identity sets unused. Two difficulties arise: client identity sets overlap only partially, and uncertain inferred relations can exert increasing influence relative to the main-task gradient. We introduce FedCrossID, a discover-then-calibrate framework. Protected Partial Identity Association (PPIA) discovers soft correspondences with an unmatched option, while Reliability-Calibrated Relation Learning (RCRL) weights detached relation targets by confidence and attenuates their influence over communication rounds. Under a protocol that withholds global identity mappings from training, FedCrossID improves a local FedPav-style baseline by 16.81 and 4.17 mAP points on Market1501 and MSMT17, reaching 49.22 ± 0.52 and 17.33 ± 0.14, respectively. Across three paired seeds, PPIA provides most of the improvement. Loss-formulation controls attribute most of the additional MSMT17 gain to the revised relation-loss formulation. Gradient diagnostics motivate phase attenuation to control the changing relative influence of relation supervision. In the tested settings, selected relations outperform degree-preserving randomized relations, including at zero identity overlap; this does not establish that the gains come from recovering true identities. Tuned constants achieve similar retrieval, and partial OT does not consistently outperform forced matching. Summary perturbation provides no formal privacy guarantee.